V2I10P04

The Future Trends of AI Approaches in Text Mining: Challenges and Innovations

Mrs. Reshmi B R^1*

Abstract

Text mining is extracting and analyzing unstructured content using the proper text pattern collected from different resources, driven by Artificial Intelligence (AI), which has developed in recent years. As the unstructured data grows, AI techniques such as deep learning, machine learning, and unsupervised learning transform how information is extracted, analyzed, and explained. Text mining authorizes businesses to gather information and create significant experiences through Artificial Intelligence (AI) algorithms. Artificial intelligence (AI) innovation shapes future trends to mine the text from various locations towards greater automation, improved circumstantial understanding, and combination with other data sources like images and audio. This leads many businesses to make proper decisions in a data-driven work. Innovations like federated learning and explainable AI (XAI) combined with neural networks are emerging to address these challenges. Moreover, advancements in quantum computing and neuromorphic processing could further enhance the efficiency and accuracy of text-mining applications. This paper explores the landscape of AI proceed towards in text mining, highlighting the challenges and innovations that will shape its future. The discussion will provide insights into how AI technologies can be leveraged to optimize information retrieval, sentiment analysis, and natural language understanding while addressing ethical and technical limitations. The applications of AI techniques are evaluated with comprehensive analysis.

Keywords:

Text Mining, Artificial Intelligence, Challenges, Innovation, Decision Making, Machine Learning, Natural Language Processing